A Tutorial on Quantum Master Equations: Tips and tricks for quantum
optics, quantum computing and beyond
- URL: http://arxiv.org/abs/2303.16449v1
- Date: Wed, 29 Mar 2023 04:23:12 GMT
- Title: A Tutorial on Quantum Master Equations: Tips and tricks for quantum
optics, quantum computing and beyond
- Authors: Francesco Campaioli, Jared H. Cole and Harini Hapuarachchi
- Abstract summary: This tutorial offers a concise and pedagogical introduction to quantum master equations.
The reader is guided through the basics of quantum dynamics with hands-on examples that build up in complexity.
The tutorial covers essential methods like the Lindblad master equation, Redfield relaxation, and Floquet theory, as well as techniques like Suzuki-Trotter expansion and numerical approaches for sparse solvers.
- Score: 0.0
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Quantum master equations are an invaluable tool to model the dynamics of a
plethora of microscopic systems, ranging from quantum optics and quantum
information processing, to energy and charge transport, electronic and nuclear
spin resonance, photochemistry, and more. This tutorial offers a concise and
pedagogical introduction to quantum master equations, accessible to a broad,
cross-disciplinary audience. The reader is guided through the basics of quantum
dynamics with hands-on examples that build up in complexity. The tutorial
covers essential methods like the Lindblad master equation, Redfield
relaxation, and Floquet theory, as well as techniques like Suzuki-Trotter
expansion and numerical approaches for sparse solvers. These methods are
illustrated with code snippets implemented in python and other languages, which
can be used as a starting point for generalisation and more sophisticated
implementations.
Related papers
- Hybrid Quantum-Classical Machine Learning with String Diagrams [49.1574468325115]
This paper develops a formal framework for describing hybrid algorithms in terms of string diagrams.
A notable feature of our string diagrams is the use of functor boxes, which correspond to a quantum-classical interfaces.
arXiv Detail & Related papers (2024-07-04T06:37:16Z) - Quantum Information Processing with Molecular Nanomagnets: an introduction [49.89725935672549]
We provide an introduction to Quantum Information Processing, focusing on a promising setup for its implementation.
We introduce the basic tools to understand and design quantum algorithms, always referring to their actual realization on a molecular spin architecture.
We present some examples of quantum algorithms proposed and implemented on a molecular spin qudit hardware.
arXiv Detail & Related papers (2024-05-31T16:43:20Z) - Lecture Notes on Quantum Electrical Circuits [49.86749884231445]
Theory of quantum electrical circuits goes under the name of circuit quantum electrodynamics or circuit-QED.
The goal of the theory is to provide a quantum description of the most relevant degrees of freedom.
These lecture notes aim at giving a pedagogical overview of this subject for theoretically-oriented Master or PhD students in physics and electrical engineering.
arXiv Detail & Related papers (2023-12-08T19:26:34Z) - Quantivine: A Visualization Approach for Large-scale Quantum Circuit
Representation and Analysis [31.203764035373677]
We develop Quantivine, an interactive system for exploring and understanding quantum circuits.
A series of novel circuit visualizations are designed to uncover contextual details such as qubit provenance, parallelism, and entanglement.
The effectiveness of Quantivine is demonstrated through two usage scenarios of quantum circuits with up to 100 qubits.
arXiv Detail & Related papers (2023-07-18T04:51:28Z) - Quantum Machine Learning: from physics to software engineering [58.720142291102135]
We show how classical machine learning approach can help improve the facilities of quantum computers.
We discuss how quantum algorithms and quantum computers may be useful for solving classical machine learning tasks.
arXiv Detail & Related papers (2023-01-04T23:37:45Z) - Modern applications of machine learning in quantum sciences [51.09906911582811]
We cover the use of deep learning and kernel methods in supervised, unsupervised, and reinforcement learning algorithms.
We discuss more specialized topics such as differentiable programming, generative models, statistical approach to machine learning, and quantum machine learning.
arXiv Detail & Related papers (2022-04-08T17:48:59Z) - Quantum information and beyond -- with quantum candies [0.0]
We investigate, extend, and greatly expand here "quantum candies" (invented by Jacobs)
"quantum" candies describe some basic concepts in quantum information, including quantum bits, complementarity, the no-cloning principle, and entanglement.
These demonstrations are done in an approachable manner, that can be explained to high-school students, without using the hard-to-grasp concept of superpositions and its mathematics.
arXiv Detail & Related papers (2021-09-30T16:05:33Z) - Quantum collision models: open system dynamics from repeated
interactions [1.5293427903448022]
We present an extensive introduction to quantum collision models (CMs), also known as repeated interactions schemes.
This article could be seen as an introduction to fundamentals of open quantum systems theory since most main concepts of this are treated such as quantum maps, Lindblad master equation, steady states, POVMs, quantum trajectories and Schrodinger equation.
arXiv Detail & Related papers (2021-06-22T18:00:01Z) - The Hintons in your Neural Network: a Quantum Field Theory View of Deep
Learning [84.33745072274942]
We show how to represent linear and non-linear layers as unitary quantum gates, and interpret the fundamental excitations of the quantum model as particles.
On top of opening a new perspective and techniques for studying neural networks, the quantum formulation is well suited for optical quantum computing.
arXiv Detail & Related papers (2021-03-08T17:24:29Z) - Quantum reservoir computing: a reservoir approach toward quantum machine
learning on near-term quantum devices [0.8206877486958002]
Quantum reservoir computing is an approach to use such a complex and rich dynamics on the quantum systems as it is for temporal machine learning.
All these quantum machine learning approaches are experimentally feasible and effective on the state-of-the-art quantum devices.
arXiv Detail & Related papers (2020-11-10T04:45:52Z) - Quantum Candies and Quantum Cryptography [0.0]
We investigate, extend, and much expand here "quantum candies" (invented by Jacobs), a pedagogical model for intuitively describing some basic concepts in quantum information.
We explicitly demonstrate various additional quantum cryptography protocols using quantum candies in an approachable manner.
arXiv Detail & Related papers (2020-11-03T21:01:08Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.